Abstract
Enhancements in data capturing technology have lead to exponential growth in amounts of data being stored in information systems. This growth in turn has motivated researchers to seek new techniques for extraction of knowledge implicit or hidden in the data. In this paper, we motivate the need for an incremental data mining approach based on data structure called the itemset tree. The motivated approach is shown to be effective for solving problems related to efficiency of handling data updates, accuracy of data mining results, processing input transactions, and answering user queries. We present efficient algorithms to insert transactions into the item-set tree and to count frequencies of itemsets for queries about strength of association among items. We prove that the expected complexity of inserting a transaction is ≈ O(1), and that of frequency counting is O(n), where n is the cardinality of the domain of items.
This research was supported in part by the U.S. Department of Energy, Grant No. DE-FG02- 97ER1220, and by the Army Research Office, Grant No. DAAH04-96-1-0325, under DEPSCoR program of Advanced Research Projects Agency, Department of Defense.
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© 1999 Springer-Verlag Berlin Heidelberg
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Hafez, A., Deogun, J., Raghavan, V.V. (1999). The Item-Set Tree: A Data Structure for Data Mining. In: Mohania, M., Tjoa, A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_20
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DOI: https://doi.org/10.1007/3-540-48298-9_20
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